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<li><a class="reference internal" href="#"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.neighbors</span></code>.LocalOutlierFactor</a><ul>
<li><a class="reference internal" href="#examples-using-sklearn-neighbors-localoutlierfactor">Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.neighbors.LocalOutlierFactor</span></code></a></li>
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  <div class="section" id="sklearn-neighbors-localoutlierfactor">
<h1><a class="reference internal" href="../classes.html#module-sklearn.neighbors" title="sklearn.neighbors"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.neighbors</span></code></a>.LocalOutlierFactor<a class="headerlink" href="#sklearn-neighbors-localoutlierfactor" title="Permalink to this headline">¶</a></h1>
<dl class="class">
<dt id="sklearn.neighbors.LocalOutlierFactor">
<em class="property">class </em><code class="sig-prename descclassname">sklearn.neighbors.</code><code class="sig-name descname">LocalOutlierFactor</code><span class="sig-paren">(</span><em class="sig-param">n_neighbors=20</em>, <em class="sig-param">algorithm='auto'</em>, <em class="sig-param">leaf_size=30</em>, <em class="sig-param">metric='minkowski'</em>, <em class="sig-param">p=2</em>, <em class="sig-param">metric_params=None</em>, <em class="sig-param">contamination='auto'</em>, <em class="sig-param">novelty=False</em>, <em class="sig-param">n_jobs=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/neighbors/_lof.py#L19"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.neighbors.LocalOutlierFactor" title="Permalink to this definition">¶</a></dt>
<dd><p>Unsupervised Outlier Detection using Local Outlier Factor (LOF)</p>
<p>The anomaly score of each sample is called Local Outlier Factor.
It measures the local deviation of density of a given sample with
respect to its neighbors.
It is local in that the anomaly score depends on how isolated the object
is with respect to the surrounding neighborhood.
More precisely, locality is given by k-nearest neighbors, whose distance
is used to estimate the local density.
By comparing the local density of a sample to the local densities of
its neighbors, one can identify samples that have a substantially lower
density than their neighbors. These are considered outliers.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 0.19.</span></p>
</div>
<dl class="field-list">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl>
<dt><strong>n_neighbors</strong><span class="classifier">int, optional (default=20)</span></dt><dd><p>Number of neighbors to use by default for <a class="reference internal" href="#sklearn.neighbors.LocalOutlierFactor.kneighbors" title="sklearn.neighbors.LocalOutlierFactor.kneighbors"><code class="xref py py-meth docutils literal notranslate"><span class="pre">kneighbors</span></code></a> queries.
If n_neighbors is larger than the number of samples provided,
all samples will be used.</p>
</dd>
<dt><strong>algorithm</strong><span class="classifier">{‘auto’, ‘ball_tree’, ‘kd_tree’, ‘brute’}, optional</span></dt><dd><p>Algorithm used to compute the nearest neighbors:</p>
<ul class="simple">
<li><p>‘ball_tree’ will use <a class="reference internal" href="sklearn.neighbors.BallTree.html#sklearn.neighbors.BallTree" title="sklearn.neighbors.BallTree"><code class="xref py py-class docutils literal notranslate"><span class="pre">BallTree</span></code></a></p></li>
<li><p>‘kd_tree’ will use <a class="reference internal" href="sklearn.neighbors.KDTree.html#sklearn.neighbors.KDTree" title="sklearn.neighbors.KDTree"><code class="xref py py-class docutils literal notranslate"><span class="pre">KDTree</span></code></a></p></li>
<li><p>‘brute’ will use a brute-force search.</p></li>
<li><p>‘auto’ will attempt to decide the most appropriate algorithm
based on the values passed to <a class="reference internal" href="#sklearn.neighbors.LocalOutlierFactor.fit" title="sklearn.neighbors.LocalOutlierFactor.fit"><code class="xref py py-meth docutils literal notranslate"><span class="pre">fit</span></code></a> method.</p></li>
</ul>
<p>Note: fitting on sparse input will override the setting of
this parameter, using brute force.</p>
</dd>
<dt><strong>leaf_size</strong><span class="classifier">int, optional (default=30)</span></dt><dd><p>Leaf size passed to <a class="reference internal" href="sklearn.neighbors.BallTree.html#sklearn.neighbors.BallTree" title="sklearn.neighbors.BallTree"><code class="xref py py-class docutils literal notranslate"><span class="pre">BallTree</span></code></a> or <a class="reference internal" href="sklearn.neighbors.KDTree.html#sklearn.neighbors.KDTree" title="sklearn.neighbors.KDTree"><code class="xref py py-class docutils literal notranslate"><span class="pre">KDTree</span></code></a>. This can
affect the speed of the construction and query, as well as the memory
required to store the tree. The optimal value depends on the
nature of the problem.</p>
</dd>
<dt><strong>metric</strong><span class="classifier">string or callable, default ‘minkowski’</span></dt><dd><p>metric used for the distance computation. Any metric from scikit-learn
or scipy.spatial.distance can be used.</p>
<p>If metric is “precomputed”, X is assumed to be a distance matrix and
must be square. X may be a sparse matrix, in which case only “nonzero”
elements may be considered neighbors.</p>
<p>If metric is a callable function, it is called on each
pair of instances (rows) and the resulting value recorded. The callable
should take two arrays as input and return one value indicating the
distance between them. This works for Scipy’s metrics, but is less
efficient than passing the metric name as a string.</p>
<p>Valid values for metric are:</p>
<ul class="simple">
<li><p>from scikit-learn: [‘cityblock’, ‘cosine’, ‘euclidean’, ‘l1’, ‘l2’,
‘manhattan’]</p></li>
<li><p>from scipy.spatial.distance: [‘braycurtis’, ‘canberra’, ‘chebyshev’,
‘correlation’, ‘dice’, ‘hamming’, ‘jaccard’, ‘kulsinski’,
‘mahalanobis’, ‘minkowski’, ‘rogerstanimoto’, ‘russellrao’,
‘seuclidean’, ‘sokalmichener’, ‘sokalsneath’, ‘sqeuclidean’,
‘yule’]</p></li>
</ul>
<p>See the documentation for scipy.spatial.distance for details on these
metrics:
<a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/spatial.distance.html">https://docs.scipy.org/doc/scipy/reference/spatial.distance.html</a></p>
</dd>
<dt><strong>p</strong><span class="classifier">integer, optional (default=2)</span></dt><dd><p>Parameter for the Minkowski metric from
<code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.metrics.pairwise.pairwise_distances</span></code>. When p = 1, this
is equivalent to using manhattan_distance (l1), and euclidean_distance
(l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used.</p>
</dd>
<dt><strong>metric_params</strong><span class="classifier">dict, optional (default=None)</span></dt><dd><p>Additional keyword arguments for the metric function.</p>
</dd>
<dt><strong>contamination</strong><span class="classifier">‘auto’ or float, optional (default=’auto’)</span></dt><dd><p>The amount of contamination of the data set, i.e. the proportion
of outliers in the data set. When fitting this is used to define the
threshold on the scores of the samples.</p>
<ul class="simple">
<li><p>if ‘auto’, the threshold is determined as in the
original paper,</p></li>
<li><p>if a float, the contamination should be in the range [0, 0.5].</p></li>
</ul>
<div class="versionchanged">
<p><span class="versionmodified changed">Changed in version 0.22: </span>The default value of <code class="docutils literal notranslate"><span class="pre">contamination</span></code> changed from 0.1
to <code class="docutils literal notranslate"><span class="pre">'auto'</span></code>.</p>
</div>
</dd>
<dt><strong>novelty</strong><span class="classifier">boolean, default False</span></dt><dd><p>By default, LocalOutlierFactor is only meant to be used for outlier
detection (novelty=False). Set novelty to True if you want to use
LocalOutlierFactor for novelty detection. In this case be aware that
that you should only use predict, decision_function and score_samples
on new unseen data and not on the training set.</p>
</dd>
<dt><strong>n_jobs</strong><span class="classifier">int or None, optional (default=None)</span></dt><dd><p>The number of parallel jobs to run for neighbors search.
<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/parallel.html#joblib.parallel_backend" title="(in joblib v0.14.1.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
<code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="../../glossary.html#term-n-jobs"><span class="xref std std-term">Glossary</span></a>
for more details.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Attributes</dt>
<dd class="field-even"><dl>
<dt><strong>negative_outlier_factor_</strong><span class="classifier">numpy array, shape (n_samples,)</span></dt><dd><p>The opposite LOF of the training samples. The higher, the more normal.
Inliers tend to have a LOF score close to 1 (<code class="docutils literal notranslate"><span class="pre">negative_outlier_factor_</span></code>
close to -1), while outliers tend to have a larger LOF score.</p>
<p>The local outlier factor (LOF) of a sample captures its
supposed ‘degree of abnormality’.
It is the average of the ratio of the local reachability density of
a sample and those of its k-nearest neighbors.</p>
</dd>
<dt><strong>n_neighbors_</strong><span class="classifier">integer</span></dt><dd><p>The actual number of neighbors used for <a class="reference internal" href="#sklearn.neighbors.LocalOutlierFactor.kneighbors" title="sklearn.neighbors.LocalOutlierFactor.kneighbors"><code class="xref py py-meth docutils literal notranslate"><span class="pre">kneighbors</span></code></a> queries.</p>
</dd>
<dt><strong>offset_</strong><span class="classifier">float</span></dt><dd><p>Offset used to obtain binary labels from the raw scores.
Observations having a negative_outlier_factor smaller than <code class="docutils literal notranslate"><span class="pre">offset_</span></code>
are detected as abnormal.
The offset is set to -1.5 (inliers score around -1), except when a
contamination parameter different than “auto” is provided. In that
case, the offset is defined in such a way we obtain the expected
number of outliers in training.</p>
</dd>
</dl>
</dd>
</dl>
<p class="rubric">References</p>
<dl class="citation">
<dt class="label" id="rca479bb49841-1"><span class="brackets">Rca479bb49841-1</span></dt>
<dd><p>Breunig, M. M., Kriegel, H. P., Ng, R. T., &amp; Sander, J. (2000, May).
LOF: identifying density-based local outliers. In ACM sigmod record.</p>
</dd>
</dl>
<p class="rubric">Examples</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.neighbors</span> <span class="kn">import</span> <span class="n">LocalOutlierFactor</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="p">[[</span><span class="o">-</span><span class="mf">1.1</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.2</span><span class="p">],</span> <span class="p">[</span><span class="mf">101.1</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.3</span><span class="p">]]</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span> <span class="o">=</span> <span class="n">LocalOutlierFactor</span><span class="p">(</span><span class="n">n_neighbors</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span><span class="o">.</span><span class="n">fit_predict</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="go">array([ 1,  1, -1,  1])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span><span class="o">.</span><span class="n">negative_outlier_factor_</span>
<span class="go">array([ -0.9821...,  -1.0370..., -73.3697...,  -0.9821...])</span>
</pre></div>
</div>
<p class="rubric">Methods</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.neighbors.LocalOutlierFactor.fit" title="sklearn.neighbors.LocalOutlierFactor.fit"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit</span></code></a>(self, X[, y])</p></td>
<td><p>Fit the model using X as training data.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.neighbors.LocalOutlierFactor.get_params" title="sklearn.neighbors.LocalOutlierFactor.get_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_params</span></code></a>(self[, deep])</p></td>
<td><p>Get parameters for this estimator.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.neighbors.LocalOutlierFactor.kneighbors" title="sklearn.neighbors.LocalOutlierFactor.kneighbors"><code class="xref py py-obj docutils literal notranslate"><span class="pre">kneighbors</span></code></a>(self[, X, n_neighbors, …])</p></td>
<td><p>Finds the K-neighbors of a point.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.neighbors.LocalOutlierFactor.kneighbors_graph" title="sklearn.neighbors.LocalOutlierFactor.kneighbors_graph"><code class="xref py py-obj docutils literal notranslate"><span class="pre">kneighbors_graph</span></code></a>(self[, X, n_neighbors, mode])</p></td>
<td><p>Computes the (weighted) graph of k-Neighbors for points in X</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.neighbors.LocalOutlierFactor.set_params" title="sklearn.neighbors.LocalOutlierFactor.set_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_params</span></code></a>(self, \*\*params)</p></td>
<td><p>Set the parameters of this estimator.</p></td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="sklearn.neighbors.LocalOutlierFactor.__init__">
<code class="sig-name descname">__init__</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">n_neighbors=20</em>, <em class="sig-param">algorithm='auto'</em>, <em class="sig-param">leaf_size=30</em>, <em class="sig-param">metric='minkowski'</em>, <em class="sig-param">p=2</em>, <em class="sig-param">metric_params=None</em>, <em class="sig-param">contamination='auto'</em>, <em class="sig-param">novelty=False</em>, <em class="sig-param">n_jobs=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/neighbors/_lof.py#L165"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.neighbors.LocalOutlierFactor.__init__" title="Permalink to this definition">¶</a></dt>
<dd><p>Initialize self.  See help(type(self)) for accurate signature.</p>
</dd></dl>

<dl class="method">
<dt id="sklearn.neighbors.LocalOutlierFactor.decision_function">
<em class="property">property </em><code class="sig-name descname">decision_function</code><a class="headerlink" href="#sklearn.neighbors.LocalOutlierFactor.decision_function" title="Permalink to this definition">¶</a></dt>
<dd><p>Shifted opposite of the Local Outlier Factor of X.</p>
<p>Bigger is better, i.e. large values correspond to inliers.</p>
<p>The shift offset allows a zero threshold for being an outlier.
Only available for novelty detection (when novelty is set to True).
The argument X is supposed to contain <em>new data</em>: if X contains a
point from training, it considers the later in its own neighborhood.
Also, the samples in X are not considered in the neighborhood of any
point.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like, shape (n_samples, n_features)</span></dt><dd><p>The query sample or samples to compute the Local Outlier Factor
w.r.t. the training samples.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>shifted_opposite_lof_scores</strong><span class="classifier">array, shape (n_samples,)</span></dt><dd><p>The shifted opposite of the Local Outlier Factor of each input
samples. The lower, the more abnormal. Negative scores represent
outliers, positive scores represent inliers.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.neighbors.LocalOutlierFactor.fit">
<code class="sig-name descname">fit</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em>, <em class="sig-param">y=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/neighbors/_lof.py#L231"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.neighbors.LocalOutlierFactor.fit" title="Permalink to this definition">¶</a></dt>
<dd><p>Fit the model using X as training data.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">{array-like, sparse matrix, BallTree, KDTree}</span></dt><dd><p>Training data. If array or matrix, shape [n_samples, n_features],
or [n_samples, n_samples] if metric=’precomputed’.</p>
</dd>
<dt><strong>y</strong><span class="classifier">Ignored</span></dt><dd><p>not used, present for API consistency by convention.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">object</span></dt><dd></dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.neighbors.LocalOutlierFactor.fit_predict">
<em class="property">property </em><code class="sig-name descname">fit_predict</code><a class="headerlink" href="#sklearn.neighbors.LocalOutlierFactor.fit_predict" title="Permalink to this definition">¶</a></dt>
<dd><p>“Fits the model to the training set X and returns the labels.</p>
<p>Label is 1 for an inlier and -1 for an outlier according to the LOF
score and the contamination parameter.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like, shape (n_samples, n_features), default=None</span></dt><dd><p>The query sample or samples to compute the Local Outlier Factor
w.r.t. to the training samples.</p>
</dd>
<dt><strong>y</strong><span class="classifier">Ignored</span></dt><dd><p>not used, present for API consistency by convention.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>is_inlier</strong><span class="classifier">array, shape (n_samples,)</span></dt><dd><p>Returns -1 for anomalies/outliers and 1 for inliers.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.neighbors.LocalOutlierFactor.get_params">
<code class="sig-name descname">get_params</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">deep=True</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/base.py#L173"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.neighbors.LocalOutlierFactor.get_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Get parameters for this estimator.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>deep</strong><span class="classifier">bool, default=True</span></dt><dd><p>If True, will return the parameters for this estimator and
contained subobjects that are estimators.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>params</strong><span class="classifier">mapping of string to any</span></dt><dd><p>Parameter names mapped to their values.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.neighbors.LocalOutlierFactor.kneighbors">
<code class="sig-name descname">kneighbors</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X=None</em>, <em class="sig-param">n_neighbors=None</em>, <em class="sig-param">return_distance=True</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/neighbors/_base.py#L531"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.neighbors.LocalOutlierFactor.kneighbors" title="Permalink to this definition">¶</a></dt>
<dd><p>Finds the K-neighbors of a point.
Returns indices of and distances to the neighbors of each point.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like, shape (n_queries, n_features),                 or (n_queries, n_indexed) if metric == ‘precomputed’</span></dt><dd><p>The query point or points.
If not provided, neighbors of each indexed point are returned.
In this case, the query point is not considered its own neighbor.</p>
</dd>
<dt><strong>n_neighbors</strong><span class="classifier">int</span></dt><dd><p>Number of neighbors to get (default is the value
passed to the constructor).</p>
</dd>
<dt><strong>return_distance</strong><span class="classifier">boolean, optional. Defaults to True.</span></dt><dd><p>If False, distances will not be returned</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>neigh_dist</strong><span class="classifier">array, shape (n_queries, n_neighbors)</span></dt><dd><p>Array representing the lengths to points, only present if
return_distance=True</p>
</dd>
<dt><strong>neigh_ind</strong><span class="classifier">array, shape (n_queries, n_neighbors)</span></dt><dd><p>Indices of the nearest points in the population matrix.</p>
</dd>
</dl>
</dd>
</dl>
<p class="rubric">Examples</p>
<p>In the following example, we construct a NeighborsClassifier
class from an array representing our data set and ask who’s
the closest point to [1,1,1]</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">samples</span> <span class="o">=</span> <span class="p">[[</span><span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.</span><span class="p">,</span> <span class="o">.</span><span class="mi">5</span><span class="p">,</span> <span class="mf">0.</span><span class="p">],</span> <span class="p">[</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="o">.</span><span class="mi">5</span><span class="p">]]</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.neighbors</span> <span class="kn">import</span> <span class="n">NearestNeighbors</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">neigh</span> <span class="o">=</span> <span class="n">NearestNeighbors</span><span class="p">(</span><span class="n">n_neighbors</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">neigh</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">samples</span><span class="p">)</span>
<span class="go">NearestNeighbors(n_neighbors=1)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">neigh</span><span class="o">.</span><span class="n">kneighbors</span><span class="p">([[</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">]]))</span>
<span class="go">(array([[0.5]]), array([[2]]))</span>
</pre></div>
</div>
<p>As you can see, it returns [[0.5]], and [[2]], which means that the
element is at distance 0.5 and is the third element of samples
(indexes start at 0). You can also query for multiple points:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="p">[[</span><span class="mf">0.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">],</span> <span class="p">[</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">]]</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">neigh</span><span class="o">.</span><span class="n">kneighbors</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">return_distance</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="go">array([[1],</span>
<span class="go">       [2]]...)</span>
</pre></div>
</div>
</dd></dl>

<dl class="method">
<dt id="sklearn.neighbors.LocalOutlierFactor.kneighbors_graph">
<code class="sig-name descname">kneighbors_graph</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X=None</em>, <em class="sig-param">n_neighbors=None</em>, <em class="sig-param">mode='connectivity'</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/neighbors/_base.py#L705"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.neighbors.LocalOutlierFactor.kneighbors_graph" title="Permalink to this definition">¶</a></dt>
<dd><p>Computes the (weighted) graph of k-Neighbors for points in X</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like, shape (n_queries, n_features),                 or (n_queries, n_indexed) if metric == ‘precomputed’</span></dt><dd><p>The query point or points.
If not provided, neighbors of each indexed point are returned.
In this case, the query point is not considered its own neighbor.</p>
</dd>
<dt><strong>n_neighbors</strong><span class="classifier">int</span></dt><dd><p>Number of neighbors for each sample.
(default is value passed to the constructor).</p>
</dd>
<dt><strong>mode</strong><span class="classifier">{‘connectivity’, ‘distance’}, optional</span></dt><dd><p>Type of returned matrix: ‘connectivity’ will return the
connectivity matrix with ones and zeros, in ‘distance’ the
edges are Euclidean distance between points.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>A</strong><span class="classifier">sparse graph in CSR format, shape = [n_queries, n_samples_fit]</span></dt><dd><p>n_samples_fit is the number of samples in the fitted data
A[i, j] is assigned the weight of edge that connects i to j.</p>
</dd>
</dl>
</dd>
</dl>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<dl class="simple">
<dt><a class="reference internal" href="sklearn.neighbors.NearestNeighbors.html#sklearn.neighbors.NearestNeighbors.radius_neighbors_graph" title="sklearn.neighbors.NearestNeighbors.radius_neighbors_graph"><code class="xref py py-obj docutils literal notranslate"><span class="pre">NearestNeighbors.radius_neighbors_graph</span></code></a></dt><dd></dd>
</dl>
</div>
<p class="rubric">Examples</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">]]</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.neighbors</span> <span class="kn">import</span> <span class="n">NearestNeighbors</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">neigh</span> <span class="o">=</span> <span class="n">NearestNeighbors</span><span class="p">(</span><span class="n">n_neighbors</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">neigh</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="go">NearestNeighbors(n_neighbors=2)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">A</span> <span class="o">=</span> <span class="n">neigh</span><span class="o">.</span><span class="n">kneighbors_graph</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">A</span><span class="o">.</span><span class="n">toarray</span><span class="p">()</span>
<span class="go">array([[1., 0., 1.],</span>
<span class="go">       [0., 1., 1.],</span>
<span class="go">       [1., 0., 1.]])</span>
</pre></div>
</div>
</dd></dl>

<dl class="method">
<dt id="sklearn.neighbors.LocalOutlierFactor.predict">
<em class="property">property </em><code class="sig-name descname">predict</code><a class="headerlink" href="#sklearn.neighbors.LocalOutlierFactor.predict" title="Permalink to this definition">¶</a></dt>
<dd><p>Predict the labels (1 inlier, -1 outlier) of X according to LOF.</p>
<p>This method allows to generalize prediction to <em>new observations</em> (not
in the training set). Only available for novelty detection (when
novelty is set to True).</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like, shape (n_samples, n_features)</span></dt><dd><p>The query sample or samples to compute the Local Outlier Factor
w.r.t. to the training samples.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>is_inlier</strong><span class="classifier">array, shape (n_samples,)</span></dt><dd><p>Returns -1 for anomalies/outliers and +1 for inliers.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.neighbors.LocalOutlierFactor.score_samples">
<em class="property">property </em><code class="sig-name descname">score_samples</code><a class="headerlink" href="#sklearn.neighbors.LocalOutlierFactor.score_samples" title="Permalink to this definition">¶</a></dt>
<dd><p>Opposite of the Local Outlier Factor of X.</p>
<p>It is the opposite as bigger is better, i.e. large values correspond
to inliers.</p>
<p>Only available for novelty detection (when novelty is set to True).
The argument X is supposed to contain <em>new data</em>: if X contains a
point from training, it considers the later in its own neighborhood.
Also, the samples in X are not considered in the neighborhood of any
point.
The score_samples on training data is available by considering the
the <code class="docutils literal notranslate"><span class="pre">negative_outlier_factor_</span></code> attribute.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like, shape (n_samples, n_features)</span></dt><dd><p>The query sample or samples to compute the Local Outlier Factor
w.r.t. the training samples.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>opposite_lof_scores</strong><span class="classifier">array, shape (n_samples,)</span></dt><dd><p>The opposite of the Local Outlier Factor of each input samples.
The lower, the more abnormal.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.neighbors.LocalOutlierFactor.set_params">
<code class="sig-name descname">set_params</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">**params</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/base.py#L205"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.neighbors.LocalOutlierFactor.set_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Set the parameters of this estimator.</p>
<p>The method works on simple estimators as well as on nested objects
(such as pipelines). The latter have parameters of the form
<code class="docutils literal notranslate"><span class="pre">&lt;component&gt;__&lt;parameter&gt;</span></code> so that it’s possible to update each
component of a nested object.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>**params</strong><span class="classifier">dict</span></dt><dd><p>Estimator parameters.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">object</span></dt><dd><p>Estimator instance.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

</dd></dl>

<div class="section" id="examples-using-sklearn-neighbors-localoutlierfactor">
<h2>Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.neighbors.LocalOutlierFactor</span></code><a class="headerlink" href="#examples-using-sklearn-neighbors-localoutlierfactor" title="Permalink to this headline">¶</a></h2>
<div class="sphx-glr-thumbcontainer" tooltip="This example shows characteristics of different anomaly detection algorithms on 2D datasets. Da..."><div class="figure align-default" id="id2">
<img alt="../../_images/sphx_glr_plot_anomaly_comparison_thumb.png" src="../../_images/sphx_glr_plot_anomaly_comparison_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/plot_anomaly_comparison.html#sphx-glr-auto-examples-plot-anomaly-comparison-py"><span class="std std-ref">Comparing anomaly detection algorithms for outlier detection on toy datasets</span></a></span><a class="headerlink" href="#id2" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="The Local Outlier Factor (LOF) algorithm is an unsupervised anomaly detection method which comp..."><div class="figure align-default" id="id3">
<img alt="../../_images/sphx_glr_plot_lof_outlier_detection_thumb.png" src="../../_images/sphx_glr_plot_lof_outlier_detection_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/neighbors/plot_lof_outlier_detection.html#sphx-glr-auto-examples-neighbors-plot-lof-outlier-detection-py"><span class="std std-ref">Outlier detection with Local Outlier Factor (LOF)</span></a></span><a class="headerlink" href="#id3" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="The Local Outlier Factor (LOF) algorithm is an unsupervised anomaly detection method which comp..."><div class="figure align-default" id="id4">
<img alt="../../_images/sphx_glr_plot_lof_novelty_detection_thumb.png" src="../../_images/sphx_glr_plot_lof_novelty_detection_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/neighbors/plot_lof_novelty_detection.html#sphx-glr-auto-examples-neighbors-plot-lof-novelty-detection-py"><span class="std std-ref">Novelty detection with Local Outlier Factor (LOF)</span></a></span><a class="headerlink" href="#id4" title="Permalink to this image">¶</a></p>
</div>
</div><div class="clearer"></div></div>
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